There is no doubt that the Industrial Internet of Things (IIoT) technology segment shows huge promise, with billions or even trillions of dollars in savings and efficiency being introduced. But how do managers at organizations implementing IIoT actually track these returns?

As an IT or operations professional implementing IIoT, you will need to tell your boss or shareholders where the return of Investment (ROI) is going to come from, before you invest. Building a case and a methodology for tracking IIoT ROI may be even as important as the ROI itself.

Let’s take a deeper look at the potential returns in IIoT technology and how you prove it.

IIoT ROI Potential

First, let’s look at the potential IIoT return and where it is coming to come from. There is no doubt that the IIoT is going to provide value — it already has done that. For example, smart buildings are showing proven savings in energy and efficiency. The trucking industry is improving on maintenance and tracking costs.

Consulting firm McKinsey, in a bottom-up analysis, concluded that the IoT in total has a potential economic impact of $3.9 trillion to $11.1 trillion a year by 2025. The highest of these gains will likely occur in factories, says McKinsey, accounting for $1.2 trillion to $3.7 trillion in value accrued.

There is no doubt that in the manufacturing sector, IIoT technology has lots of leverage to compound returns. The National Association of Purchasing Managers (NAPM) estimates that for every $1.00 spent in manufacturing, another $1.81 is added to the economy. In the most recent data, manufacturers contributed about $2 trillion to the U.S. economy alone. Globally, there are $12 trillion in manufactured goods. Just a 2 percent improvement in efficiency would yield $240 billion in benefit.

Tracking Industrial Data Returns

Where is most of the value being created from IoT and IIoT? In short, data. Collecting and analyzing vast amounts of data can lead to radically improved business efficiency.

There is no doubt that IIoT has enabled a flood of data in the manufacturing and industrial sectors alone. IHS Markit projects 30.7 billion IoT devices for 2020, and Gartner expects 20.8 billion by that time (excluding smartphones, tablets, and computers). IDC expects 28.1 billion connected devices not including the consumer devices metioned.

Now think about all the data generated from industrial devices – it’s almost impossible to calculate. For example, a twin-engine Boeing 737 aircraft generates 333 GB of data per minute per engine, according to Boeing, which means a flight from Los Angeles to New York generates roughly 200 TB of data. The energy industry estimates that drilling rigs produce 7 to 8 TB of operational data a day.

This gets to the heart of the issue of IIot: It’s about extracting, using, and managing data. Once this data is collected, it needs to be analyzed and used to produce business results.

In one example, Bsquare partner Kenworth, the global leader in heavy-duty trucking, implemented IIoT data tracking and analysis technology to maximize fleet uptime and speed repairs and warranty processing. Using the DataV Predict and DataV Repair applications, the company was able to predict failures and implement adaptive diagnostics within their operations.

The process at Kenworth can track ROI by measuring the reduction in maintenance on the fleet, boosting uptime, and in the increase in revenue miles per vehicle.

Another example can be seen at a major oil refinery. At Ergon Refining’s Vicksburg, Miss., facility, the company used wireless acoustic transmitters to monitor valves controlling gas flows to flare stacks in refineries. This process helped reduce hydrocarbon losses by $3 million annually with detection and repair of faulty valves, according to an article in Control Engineering. Managers estimate that the project provided an ROI of 271% annualized over 20 years, paying for itself in just five months

In another example of using Bsquare technology, world-leading technology and services company Itron deployed more than 150 million communication modules to more than 8,000 customers to enable smart energy meters to improve the reliability and efficiency of the energy grid.

By deploying smart meters, utilities can gain visibility and control into their distribution networks, for example by pinpointing failures. The success can be realized by measuring an increase in grid availability, reducing costs associated with multiple truck rolls, and at the same time increasing customer satisfaction. Revenue losses associated with energy theft can also be stopped more quickly by identifying threats. An ROI on the project can be proven by measuring these specific impacts.

How Fast to ROI?

In many of the cases outlined above — as well as others — its important that managers not only demonstrate a quick return to justify the ROI, but that gains can be sustained over time.

For example, in the case of Paccar, gains in reducing truck maintenance can be realized in as little as a few months. There can be as much as a 20% reduction of service time, but then results may flatten out. However, over the longer term, further gains will likely be realized down the road as the organization gathers more data and analytics programs find further adjustments to the benefit of ROI.

In a prior Bsquare blog, measuring the proof of ROI was discussed in more detail.

Companies usually have ROI targets or thresholds — “hurdles” — that must be realized to gain approval. This may include a time-based payback.

When quantifying these returns, it’s important that managers take a look at the entire process of implementing IIoT data analysis, so they might quantify gains over the longer term. For example, in the case of the smart meters, are all of the cost savings being tracked? In the truck maintenance example, unforeseen improvements can be realized when large amounts of data are collected over a period of years. More importantly, a long-term approach to transforming an organization with digital analytics can lead to insights that can help transform an organization.

This blog was contributed by guest writer R. Scott Raynovich who is the managing publisher at www.Futuriom.com 

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